Explanation:

This question refers to a system that monitors a user's emotions or expressions-in this case, identifying whether a kiosk user is annoyed-through a video feed. According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and Microsoft Learn module "Identify Azure services for computer vision," this scenario falls under facial analysis, which is a capability of Azure AI Vision or the Face API.
Facial analysis involves detecting human faces in images or video and analyzing facial features to interpret emotions, expressions, age, gender, or facial landmarks. The AI model does not try to identify who the person is but rather interprets how they appear or feel. For example, facial analysis can detect emotions such as happiness, anger, sadness, or surprise, which allows applications to infer a user's engagement or frustration level while interacting with a system.
Option review:
* Face detection: Identifies the presence and location of a face in an image but does not interpret expressions or emotions.
* Facial recognition: Matches a detected face to a known individual's identity (for authentication or security), not for emotion detection.
* Optical character recognition (OCR): Extracts text from images or scanned documents and has no relation to human emotion or facial features.
Therefore, determining whether a kiosk user is annoyed, happy, or frustrated involves emotion detection within facial analysis, making Facial analysis the correct answer.
This aligns with AI-900's definition of computer vision workloads, where facial analysis provides insights into emotions and expressions, supporting user experience optimization and customer behavior analytics.

In Azure Machine Learning designer, a low-code drag-and-drop interface, users can visually build machine learning workflows. According to the AI-900 study guide and Microsoft Learn module "Create and publish models with Azure Machine Learning designer", two key components that can be dragged onto the designer canvas are datasets and modules.
* Datasets (A): These are collections of data that serve as the input for training or evaluating models.
They can be registered in the workspace and then dragged onto the canvas for use in transformations or model training.
* Modules (D): These are prebuilt processing and modeling components that perform operations such as data cleaning, feature engineering, model training, and evaluation. Examples include "Split Data,"
"Train Model," and "Evaluate Model."
Compute (B) and Pipeline (C) are not drag-and-drop items within the designer. Compute targets are infrastructure resources used to run the pipeline, while a pipeline represents the overall workflow, not a component that can be added like a dataset or module.
Hence, the correct answers are A. Dataset and D. Module.
Reference:Microsoft Learn - Create a machine learning model with Azure Machine Learning designer

Explanation:

Box 1: No
Box 2: Yes
Box 3: Yes
Anomaly detection encompasses many important tasks in machine learning:
Identifying transactions that are potentially fraudulent.
Learning patterns that indicate that a network intrusion has occurred.
Finding abnormal clusters of patients.
Checking values entered into a system.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/anomaly-detection

Explanation:

According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and the Microsoft Learn module "Identify guiding principles for responsible AI", responsible AI is built upon six foundational principles: Fairness, Reliability and Safety, Privacy and Security, Inclusiveness, Transparency, and Accountability. Each principle serves to guide the ethical design, deployment, and management of artificial intelligence systems.
* Fairness - This principle ensures that AI systems treat all people fairly and do not discriminate based on personal attributes such as gender, race, or age. The Microsoft Learn content emphasizes that "AI systems should treat everyone fairly" and that organizations must evaluate datasets and model outputs for bias. In this scenario, "The system must not discriminate based on gender, race" clearly aligns with Fairness because it directly addresses equitable treatment and unbiased decision-making.
* Privacy and Security - Microsoft's responsible AI framework stresses that "AI systems must be secure and respect privacy." This means personal data should be safeguarded, processed lawfully, and visible only to authorized users. The statement "Personal data must be visible only to approved users" reflects the importance of protecting sensitive information and controlling access-precisely the intent of the Privacy and Security principle.
* Transparency - Transparency refers to ensuring that users understand how AI systems operate and make decisions. Microsoft notes that "AI systems should be understandable and users should be able to know why decisions are made." The requirement "Automated decision-making processes must be recorded so that approved users can identify why a decision was made" directly supports this principle.
Transparency promotes trust and accountability by documenting the reasoning behind AI outputs.
Reliability and Safety, though another core principle, does not directly relate to any of the provided statements in this question.

Explanation:

The correct matches are based on the Microsoft Azure AI Fundamentals (AI-900) Official Study Guide and the Microsoft Learn module "Explore computer vision in Microsoft Azure." These materials explain that facial recognition tasks can be categorized into four major operations: verification, identification, similarity, and grouping. Each task serves a distinct purpose in facial recognition scenarios.
* Verification - "Do two images of a face belong to the same person?"The verification task determines whether two facial images represent the same individual. Azure Face API compares the facial features and returns a confidence score indicating the likelihood that the two faces belong to the same person.
* Similarity - "Does this person look like other people?"The similarity task compares a face against a collection of faces to find visually similar individuals. It does not confirm identity but measures how closely two or more faces resemble each other.
* Grouping - "Do all the faces belong together?"Grouping organizes a set of unknown faces into clusters based on similar facial features. This is used when identities are not known beforehand, helping discover potential duplicates or visually similar clusters within an image dataset.
* Identification - "Who is this person in this group of people?"The identification task is used when the system tries to determine who a specific person is by comparing their face against a known collection (face database or gallery). It returns the identity that best matches the input face.
According to Microsoft's AI-900 training, these tasks form the basis of Azure Face API's capabilities. Each helps solve a different type of facial recognition problem-from matching pairs to discovering unknown identities-making them essential components of responsible AI-based vision systems.